Stock Market Prediction
42 papers with code • 3 benchmarks • 5 datasets
Libraries
Use these libraries to find Stock Market Prediction models and implementations
StockNet
Most implemented papers
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
google-research/bert
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NAACL 2019
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.
RoBERTa: A Robustly Optimized BERT Pretraining Approach
pytorch/fairseq
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Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging.
SKEP: Sentiment Knowledge Enhanced Pre-training for Sentiment Analysis
baidu/Senta
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ACL 2020
In particular, the prediction of aspect-sentiment pairs is converted into multi-label classification, aiming to capture the dependency between words in a pair.
Sentiment Analysis of Twitter Data for Predicting Stock Market Movements
harishpuvvada/BitCoin-Value-Predictor
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In this paper, we have applied sentiment analysis and supervised machine learning principles to the tweets extracted from twitter and analyze the correlation between stock market movements of a company and sentiments in tweets.
Revisiting Pre-Trained Models for Chinese Natural Language Processing
ymcui/MacBERT
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Findings of the Association for Computational Linguistics 2020
Bidirectional Encoder Representations from Transformers (BERT) has shown marvelous improvements across various NLP tasks, and consecutive variants have been proposed to further improve the performance of the pre-trained language models.
FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance
AI4Finance-Foundation/FinRL
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In this paper, we introduce a DRL library FinRL that facilitates beginners to expose themselves to quantitative finance and to develop their own stock trading strategies.
Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction
Stock trend prediction plays a critical role in seeking maximized profit from stock investment.
Twitter mood predicts the stock market
A Granger causality analysis and a Self-Organizing Fuzzy Neural Network are then used to investigate the hypothesis that public mood states, as measured by the OpinionFinder and GPOMS mood time series, are predictive of changes in DJIA closing values.
Stock Price Correlation Coefficient Prediction with ARIMA-LSTM Hybrid Model
Predicting the price correlation of two assets for future time periods is important in portfolio optimization.
Temporal Relational Ranking for Stock Prediction
hennande/Temporal_Relational_Stock_Ranking
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Our RSR method advances existing solutions in two major aspects: 1) tailoring the deep learning models for stock ranking, and 2) capturing the stock relations in a time-sensitive manner.
